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Surface electromyography is a non-invasive electrical potential from human muscle. The signals are easily disturbed by many interference and noise. Lower frequency range of surface electromyography needs to be eliminated due to motion artefacts, instability and unpredictable fluctuation. There is no standard range of corner frequency and it highly depends on the dynamic movement during surface EMG signal collection. Adding high pass filter active or digital may require more cost or computation; yet, sometimes is unable to remove all corner frequency noises. This paper presents a new method in filtering the remaining corner frequency after conventional filter is employed, using Undecimated Wavelet Transform (UWT). Results show that the decomposing surface EMG signals, which are at 1400 Hz frequency sampling of surface EMG signal and at six levels decomposition, are suitable for setting the highest corner frequency within the range of 0-20Hz. Hard threshold de-noising technique and method of UWT reconstruction enable the easy removal of 20Hz corner frequency noise of surface EMG employing the stated parameter.

Surface electromyography (SEMG) signals are widely used in fatigue identification. Fatigue after high intensity exercise and sports training needs to be balanced with rest to allow biochemical reactions during sports activity to return to a normal level. Inadequate rest leads to prolonged fatigue (PF) conditions such as musculoskeletal disorder, unexplained lethargy and performance decrement. Continuous sports training under these conditions may lead to injury. Fatigue identification at this stage is crucial since changes in amplitude and frequency of SEMG may determine whether the player is under normal fatigue (NF) or PF condition. During data collection, there are many interferences and noises which can reduce signal to noise ratio (SNR) of SEMG and affect PF detection. This paper pre-processed SEMG signals using Stationary Wavelet Transform (SWT) 'db' 45 with different threshold (Th) estimation techniques of de-noising such as RigRSURE, HeurSURE, minimax, universal threshold and a new estimation of threshold method which is based on a baseline of SEMG decomposition details. Naïve Bayes classification results using time and frequency features indicate that the new estimation of threshold method have the highest accuracy (98%), compared to RigRSURE (85%), HuerSURE (68%), Universal Threshold (74%) and minimax (76%).

Sports training is very important to athletes in improving and maintaining their performances. It commonly involves high intensity exercise and requires longer time for fatigue to recover, compares to normal activity. During training, adequate rest is essential to allow recuperation and build body strength. Unfortunately, inadequate rest exposes the body to prolonged fatigue (PF). Hence, this condition needs to be managed accordingly to avoid chronic fatigue syndrome. Recent findings indicate that there are strong characteristics on surface EMG under PF conditions. Currently, the assessment is limited to glycogen breakdown, the existence of lactate and soreness. In this study, twenty participants were recruited to perform five days intensive training (IT) to induce more PF signs. The IT conducted was based on Bruce Protocol treadmill test. It was discovered that the IT successfully induces soreness, unexplained lethargy and performance decrement. Surface EMG were collected from rectus femoris muscle during daily pre and post treadmill tests. Four features were extracted from the surface EMG; mean frequency (Fmean), median frequency (Fmed), root mean square (RMS) and mean absolute value (MAV). The results indicated that all features during post exercise had greater value under PF condition and it was significant at P<;0.05. The features then were classified in accordance to Bayes. The results also showed that Fmed and MAV features offered good performance with 83.1% accuracy, 84.6% specificity and 80% of precision.

High quality of surface electromyography is vital during investigation on muscle activity. Low quality of surface EMG signals causes extracted signals to be inaccurate and lead to misinterpretation and misclassification of the signals. A surface EMG signal quality is determined by the ratio of muscle contraction to its baseline during muscle relaxation period. Baseline noises are originated from powerline, cable motion artefact, electronics of the amplification systems and skin-electrode interface. The noises are quite difficult to be removed by digital or active filter since they do not have specific frequency range like powerline interference and corner frequency noise. However, wavelet de-noising enables users to remove noise by accessing both frequency and time information. Baseline surface EMG noise is possible to be removed by estimating de-noise threshold based on mean absolute value and root mean square of its baseline. The result of this study shows that the proposed estimation of threshold method is better than the conventional threshold setting.

Ability of wavelet transform in accessing time and frequency information at the same time make it widely used in analyzing bio-signals like electromyography (EMG). Discrete wavelet transforms (DWT) and stationary wavelet transform (SWT) are examples of analysis based on wavelet. Both analyses are based on decomposition technique and splitting signals into few frequency band. The different is DWT will down sample resolution into half at each decomposition level, while SWT is not. This paper is investigating both analyses in its ability on de-noising process of EMG using the same properties. The signals will be decomposed into five level of decomposition using 'db20', and de-noised using the same threshold setting. The performance will be evaluated based on its signals to noise ratio and muscle fatigue detection. Results show that de-noising process through SWT give better signals to ratio. Inability in DWT removed 20Hz corner frequency in several reading lead to misinterpretation in fatigue detection.

Electromyography (EMG) is one of the indirect tools in indexing fatigue. Fatigue can be detected when there are changes on amplitude and frequency. However, various outcomes from literature make researchers conclude that EMG is not a reliable tool to measure fatigue. This paper investigates EMG behavior of biceps femoris in median frequency and mean absolute value during five days of Bruce Protocol treadmill test. Before that, surface EMG signals are filtered using band pass filter cut-off at 20-500Hz and are de-noised using db45 1-decimated wavelet transform. Five participants achieved more than 85% of their maximal heart rate during the running activity. The authors also consider other markers of fatigue such as performance, muscle soreness and lethargy as indicators to adaptation and maladaptation conditions. Result shows that turning points of median frequency and mean absolute value are very significant in indexing fatigue and indicators to adaptation of resistive training.

Fatigue detection through electromyography analysis has been in use since 1912 when it was discovered by H. Piper. Until now, there has been much research on muscle fatigue through electromyography, both invasively and non-invasively. There are evolutions of the electromyography based approach to detect fatigue in various aspects including methodology, analysis, application and the evaluation of fatigue itself. This paper intends to provide a brief review of previous research as this will help beginning researcher to find further direction in this research area.

Electromyography signals is one of the most important studies of muscle function used in wide area including kinesiology, rehabilitation, and occupational and sports medicine. The measurement of electromyogram always requires multichannel as every movement will involve a group of muscles. Commercial single or multichannel surface electromyogram data acquisition system is very expensive for beginner researcher. Some of the systems are available in bulky device, heavy and not portable. As electromyogram detected once muscle is active and contract, a wearable device is desired since several movement will involve during measurement is taken. Therefore, this paper presents architecture of low cost and wearable multichannel surface electromyogram data acquisition system. It is light weight, low power consumption and using stacking system as numbers of electromyography channels used can be varied based on application.

Each nerves and muscles of human body will generate potentials, voltages, and electrical field strength and known as bio signals such as electrocardiograph (ECG), electromyography (EMG), electroencephalogram (EEG), and electrooculogram (EOG). The signals may be acquired through non-invasive surface Ag/Ag Cl applications although the generated signals exist in very low levels of voltages. All the bio signals may be obtained from basic designs of instrumentation amplifier with the help of Ag/Ag Cl electrodes. However, different frequency band, interference, amplitude and strength may give huge challenges to the acquisition system. This paper provides instrumentation amplification design, include with the design concept to allow for flexible channels. There are lots of advantages from the proposed design such as minimal additional cost for additional channels used and circuit design modification or replacement. To suits bio-signals frequency bandwidth need, digital filter design in Labview software is added, as a part of acquisition system and to discard unwanted signals outside frequency bandwidth.. For now, the design is able to demonstrate to acquire two bio-potentials signals; ECG and EMG signals.

Electromyography is one of the tools to measure fatigue indirectly. Widely research on electromyography and fatigue have been done include onset of fatigue and transition of fatigue during physical activity. The investigation not focusing on the muscle only, but other physiological indicator also involved to see the correlation. However, the information of electromyography behavior after activity is very limited. This paper provides investigation on lower extremities muscle activities before and after a single treadmill test. Fatigue mapping is done for electromyography and heart rate as physiological indicators. Two analyses based on time and time-frequency are chosen to process the signals. The results show that the behavior of electromyography amplitude and heart rate are back to normal condition (recover) in two to three hours, and median frequency shows faster recovery.

Recording of biopotential signals is uneasy as interference and noise can come from the preparation of the subject, environment setting and other biological signal. The circuit design in acquisition system must good and meets specification as is not distort original signal and safe to patient. Classic design of biosignal acquisition system usually use third electrode as reference potential. The third electrode is not picking up the biological signal and placed quite far from the measured signal as it can help to remove common mode signal. However, sometime the third electrode will limit patient movement since some of the experiment requires the patient or subject do several kind of movement during signals recording. Therefore, two electrodes system without third electrode may provide mobility and comfort since number of electrode patched on the body is reduced. Though several previous researches have implemented the two electrodes system, but they only tested on electrocardiogram. Hence, the main contribution of this paper is investigating the ability and performance of the proposed system on fetal electrocardiogram and electromyogram detection since the challenges to detect both signals are different compare to electrocardiogram. The electrocardiogram, fetal electrocardiogram and electromyogram are successfully detected using the propose system but with several filtering stages. The performance evaluation include signal to noise ratio after the filtering stage and accuracy of the signal detection.